Slot filling (SF) is a process by which predefined attributes (slot values) of a target entity are extracted from a large collection of natural language text. Generally, SF involves two steps: a Named Entity Recognition (NER) step by which entities of interest (which can be organizations, persons, numerical values, locations, etc.) are located, and a relationship extraction step by which relationships between the target entity and other entities of interest (potentially the slot values) are determined.
One requirement of SF is the presence of multiple independent documents in which the desired information between the target entity and the slot values being extracted is mentioned. This is because SF requires that the target relationship, i.e., the relationship of interest between the target entity and the slot being filled, to be more prominent than other relationships between the target entity and the slot being filled. For example, if the target relationship is that Obama ‘was born in’ Hawaii, then SF would need that this relationship be present more prominently in the text collection than other relationships such as Obama ‘lived in’ Hawaii or Obama ‘visited’ Hawaii, for example.
Another requirement of SF is that the entities, particularly the target entity, be explicitly mentioned in the text. Further, SF typically operates on unstructured text, i.e., plain natural language sentences.
Besides, relationship extraction in SF typically relies on machine learning techniques that require a large amount of training data, that is, sentences in which the entities are tagged (typically manually) and in which their relationships appear.
These requirements are restrictive for certain applications. For example, in some cases, the target entity may be described in a small corpora, e.g., a single document, and the information to be extracted (i.e., the information between the target entity and the slot value) may be mentioned only a few times (e.g., less than 3 times) in the document.
Additionally, the document may be a descriptive document, in which the target entity is mentioned implicitly (latent) or with an alias. For instance, in an investment fund prospectus, the name of the fund (i.e., the target entity) is latent or referred to by an alias. To fill in a slot (e.g., the fund's depository) from the investment fund prospectus, sentences that do not mention the name of the fund (e.g., “The Depository assigned is Bank A”) or sentences that refer to the name of the fund by an alias (e.g., “The Depository of the fund is Bank B”—the “fund” being the alias) need to be used. This is in contrast to SF in which the corpora explicitly states the target entity. For example, if the slot being filled is country_of_birth for the target entity Michelle Obama, the corpora used in SF would include sentences such as “Michelle Obama was born in Chicago, Ill.” which explicitly state the target entity.
The information may also be provided as semi-structured text in some cases. For example, instead of having the information in a full sentence, the relationship between the entities may be provided as a form (e.g., “Depository: Bank A”).
Finally, tagged data (which is typically tagged manually), large amounts of which is needed for SF, may not always be available and is expensive and time consuming to generate.
There is a need therefore for an information extraction method that addresses one or more of the above described deficiencies of SF.